Overview

Dataset statistics

Number of variables24
Number of observations91646
Missing cells222970
Missing cells (%)10.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 MiB
Average record size in memory192.0 B

Variable types

Numeric6
Text9
Unsupported2
Categorical7

Alerts

aff_code has constant value ""Constant
page_channel has constant value ""Constant
fuel_type is highly imbalanced (79.9%)Imbalance
msrp has 24089 (26.3%) missing valuesMissing
local_zone has 91646 (100.0%) missing valuesMissing
interior_color has 4872 (5.3%) missing valuesMissing
price_badge has 91646 (100.0%) missing valuesMissing
trim has 1592 (1.7%) missing valuesMissing
dealer_name has 941 (1.0%) missing valuesMissing
dealer_zip has 941 (1.0%) missing valuesMissing
mileage has 2366 (2.6%) missing valuesMissing
cat has 1301 (1.4%) missing valuesMissing
exterior_color has 1042 (1.1%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
local_zone is an unsupported type, check if it needs cleaning or further analysisUnsupported
price_badge is an unsupported type, check if it needs cleaning or further analysisUnsupported
msrp has 19897 (21.7%) zerosZeros
mileage has 3630 (4.0%) zerosZeros

Reproduction

Analysis started2024-05-20 04:58:36.607720
Analysis finished2024-05-20 04:58:48.458928
Duration11.85 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct91646
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45822.5
Minimum0
Maximum91645
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:48.589615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4582.25
Q122911.25
median45822.5
Q368733.75
95-th percentile87062.75
Maximum91645
Range91645
Interquartile range (IQR)45822.5

Descriptive statistics

Standard deviation26456.066
Coefficient of variation (CV)0.57735972
Kurtosis-1.2
Mean45822.5
Median Absolute Deviation (MAD)22911.5
Skewness0
Sum4.1994488 × 109
Variance6.9992341 × 108
MonotonicityStrictly increasing
2024-05-19T23:58:48.767983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
61083 1
 
< 0.1%
61103 1
 
< 0.1%
61102 1
 
< 0.1%
61101 1
 
< 0.1%
61100 1
 
< 0.1%
61099 1
 
< 0.1%
61098 1
 
< 0.1%
61097 1
 
< 0.1%
61096 1
 
< 0.1%
Other values (91636) 91636
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
91645 1
< 0.1%
91644 1
< 0.1%
91643 1
< 0.1%
91642 1
< 0.1%
91641 1
< 0.1%
91640 1
< 0.1%
91639 1
< 0.1%
91638 1
< 0.1%
91637 1
< 0.1%
91636 1
< 0.1%

msrp
Real number (ℝ)

MISSING  ZEROS 

Distinct10319
Distinct (%)15.3%
Missing24089
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean36288.707
Minimum0
Maximum329486
Zeros19897
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:48.938811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median35800
Q354315
95-th percentile87525
Maximum329486
Range329486
Interquartile range (IQR)54315

Descriptive statistics

Standard deviation32017.981
Coefficient of variation (CV)0.88231254
Kurtosis5.3755323
Mean36288.707
Median Absolute Deviation (MAD)21735
Skewness1.3289265
Sum2.4515562 × 109
Variance1.0251511 × 109
MonotonicityNot monotonic
2024-05-19T23:58:49.114901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19897
21.7%
46465 201
 
0.2%
44115 192
 
0.2%
35975 174
 
0.2%
54595 150
 
0.2%
34085 124
 
0.1%
36926 118
 
0.1%
32189 112
 
0.1%
33160 109
 
0.1%
26980 107
 
0.1%
Other values (10309) 46373
50.6%
(Missing) 24089
26.3%
ValueCountFrequency (%)
0 19897
21.7%
5895 1
 
< 0.1%
5991 1
 
< 0.1%
5995 1
 
< 0.1%
6000 3
 
< 0.1%
6188 2
 
< 0.1%
6495 2
 
< 0.1%
6995 1
 
< 0.1%
7000 2
 
< 0.1%
7705 2
 
< 0.1%
ValueCountFrequency (%)
329486 4
 
< 0.1%
326445 1
 
< 0.1%
317486 2
 
< 0.1%
311895 3
 
< 0.1%
309695 1
 
< 0.1%
300386 1
 
< 0.1%
298875 11
< 0.1%
295895 4
 
< 0.1%
295886 1
 
< 0.1%
288300 2
 
< 0.1%

year
Real number (ℝ)

Distinct65
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.1608
Minimum1936
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:49.304745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1936
5-th percentile2007
Q12019
median2023
Q32024
95-th percentile2024
Maximum2025
Range89
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.5060897
Coefficient of variation (CV)0.0032205802
Kurtosis18.433617
Mean2020.1608
Median Absolute Deviation (MAD)1
Skewness-3.3254881
Sum1.8513966 × 108
Variance42.329204
MonotonicityNot monotonic
2024-05-19T23:58:49.563132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024 43324
47.3%
2023 6765
 
7.4%
2021 6658
 
7.3%
2020 4157
 
4.5%
2022 3766
 
4.1%
2019 3289
 
3.6%
2018 3109
 
3.4%
2017 2733
 
3.0%
2016 2432
 
2.7%
2015 2059
 
2.2%
Other values (55) 13354
 
14.6%
ValueCountFrequency (%)
1936 7
 
< 0.1%
1953 1
 
< 0.1%
1957 36
< 0.1%
1959 9
 
< 0.1%
1960 15
< 0.1%
1961 4
 
< 0.1%
1964 8
 
< 0.1%
1965 25
< 0.1%
1966 7
 
< 0.1%
1967 5
 
< 0.1%
ValueCountFrequency (%)
2025 855
 
0.9%
2024 43324
47.3%
2023 6765
 
7.4%
2022 3766
 
4.1%
2021 6658
 
7.3%
2020 4157
 
4.5%
2019 3289
 
3.6%
2018 3109
 
3.4%
2017 2733
 
3.0%
2016 2432
 
2.7%
Distinct10316
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:50.045657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length102
Median length91
Mean length28.129891
Min length12

Characters and Unicode

Total characters2577992
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1682 ?
Unique (%)1.8%

Sample

1st rowChevrolet:Blazer EV:RS:2024
2nd rowRAM:ProMaster 2500:High Roof:2024
3rd rowMercedes-Benz:Sprinter 2500:High Roof:2024
4th rowHonda:CR-V:EX:2024
5th rowChevrolet:Equinox:LS:2024
ValueCountFrequency (%)
jeep:grand 2198
 
1.2%
se:2024 1645
 
0.9%
s 1542
 
0.9%
premium 1487
 
0.8%
mercedes-benz:amg 1320
 
0.7%
4matic:2024 1294
 
0.7%
chevrolet:silverado 1253
 
0.7%
volkswagen:tiguan:2.0t 1180
 
0.7%
ram:promaster 1165
 
0.7%
package:2024 1151
 
0.7%
Other values (9838) 162826
92.0%
2024-05-19T23:58:50.532919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2577992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2577992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2577992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 274938
 
10.7%
2 174780
 
6.8%
e 166055
 
6.4%
0 138456
 
5.4%
a 126354
 
4.9%
r 114485
 
4.4%
o 93474
 
3.6%
i 90624
 
3.5%
85271
 
3.3%
n 75657
 
2.9%
Other values (70) 1237898
48.0%

model
Text

Distinct1042
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:51.025416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length28
Median length24
Mean length7.2026384
Min length1

Characters and Unicode

Total characters660093
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowBlazer EV
2nd rowProMaster 2500
3rd rowSprinter 2500
4th rowCR-V
5th rowEquinox
ValueCountFrequency (%)
1500 2858
 
2.3%
grand 2399
 
1.9%
cherokee 2291
 
1.8%
2500 2233
 
1.8%
escape 1559
 
1.3%
outback 1538
 
1.2%
hybrid 1419
 
1.1%
tucson 1353
 
1.1%
amg 1346
 
1.1%
l 1324
 
1.1%
Other values (852) 105719
85.2%
2024-05-19T23:58:51.680778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 660093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 660093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 660093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 50076
 
7.6%
r 46722
 
7.1%
e 44205
 
6.7%
o 33503
 
5.1%
32393
 
4.9%
n 28752
 
4.4%
0 27108
 
4.1%
i 24309
 
3.7%
t 24169
 
3.7%
s 21715
 
3.3%
Other values (61) 327141
49.6%

local_zone
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing91646
Missing (%)100.0%
Memory size716.1 KiB

interior_color
Text

MISSING 

Distinct1456
Distinct (%)1.7%
Missing4872
Missing (%)5.3%
Memory size716.1 KiB
2024-05-19T23:58:51.993341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length62
Median length5
Mean length7.5585083
Min length1

Characters and Unicode

Total characters655882
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)0.3%

Sample

1st rowblack
2nd rowblack
3rd rowgray
4th rowmedium_ash_gray
5th rowpearl_beige
ValueCountFrequency (%)
black 34971
40.3%
gray 6214
 
7.2%
jet_black 5533
 
6.4%
ebony 4659
 
5.4%
charcoal 3771
 
4.3%
beige 1583
 
1.8%
global_black 1440
 
1.7%
titan_black 1147
 
1.3%
graphite 1036
 
1.2%
tan 969
 
1.1%
Other values (1445) 25451
29.3%
2024-05-19T23:58:52.564453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 655882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 655882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 655882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 93417
14.2%
c 68819
10.5%
l 65974
10.1%
b 65627
10.0%
k 54028
 
8.2%
e 40951
 
6.2%
_ 33488
 
5.1%
r 32603
 
5.0%
t 25005
 
3.8%
o 24986
 
3.8%
Other values (36) 150984
23.0%

aff_code
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
national
91646 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters733168
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownational
2nd rownational
3rd rownational
4th rownational
5th rownational

Common Values

ValueCountFrequency (%)
national 91646
100.0%

Length

2024-05-19T23:58:52.708720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:58:52.847595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
national 91646
100.0%

Most occurring characters

ValueCountFrequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 733168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 733168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 733168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 183292
25.0%
a 183292
25.0%
t 91646
12.5%
i 91646
12.5%
o 91646
12.5%
l 91646
12.5%

price
Real number (ℝ)

Distinct20233
Distinct (%)22.3%
Missing868
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean42623.437
Minimum0
Maximum1699800
Zeros34
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:53.023236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9995
Q123712
median34298.5
Q351465
95-th percentile93000
Maximum1699800
Range1699800
Interquartile range (IQR)27753

Descriptive statistics

Standard deviation42024.853
Coefficient of variation (CV)0.98595646
Kurtosis385.96192
Mean42623.437
Median Absolute Deviation (MAD)13318.5
Skewness12.822971
Sum3.8692703 × 109
Variance1.7660882 × 109
MonotonicityNot monotonic
2024-05-19T23:58:53.209539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9995 271
 
0.3%
14995 248
 
0.3%
18995 234
 
0.3%
19995 229
 
0.2%
16995 223
 
0.2%
12995 221
 
0.2%
15995 211
 
0.2%
17995 207
 
0.2%
13995 206
 
0.2%
51465 199
 
0.2%
Other values (20223) 88529
96.6%
(Missing) 868
 
0.9%
ValueCountFrequency (%)
0 34
< 0.1%
32 1
 
< 0.1%
1500 8
 
< 0.1%
1700 2
 
< 0.1%
1900 2
 
< 0.1%
1995 3
 
< 0.1%
2000 12
 
< 0.1%
2500 5
 
< 0.1%
2550 1
 
< 0.1%
2700 1
 
< 0.1%
ValueCountFrequency (%)
1699800 13
< 0.1%
829800 8
< 0.1%
709800 13
< 0.1%
639800 2
 
< 0.1%
599800 8
< 0.1%
599000 1
 
< 0.1%
579999 2
 
< 0.1%
575800 1
 
< 0.1%
569900 2
 
< 0.1%
569895 2
 
< 0.1%

price_badge
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing91646
Missing (%)100.0%
Memory size716.1 KiB

trim
Text

MISSING 

Distinct2173
Distinct (%)2.4%
Missing1592
Missing (%)1.7%
Memory size716.1 KiB
2024-05-19T23:58:53.643703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length75
Median length66
Mean length7.7482399
Min length1

Characters and Unicode

Total characters697760
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)0.3%

Sample

1st rowRS
2nd rowHigh Roof
3rd rowHigh Roof
4th rowEX
5th rowLS
ValueCountFrequency (%)
base 9266
 
6.5%
premium 5733
 
4.0%
s 4647
 
3.3%
se 4486
 
3.2%
limited 4377
 
3.1%
sel 3536
 
2.5%
4matic 3523
 
2.5%
sport 3091
 
2.2%
2.0t 2619
 
1.8%
plus 2284
 
1.6%
Other values (1221) 98094
69.2%
2024-05-19T23:58:54.267960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 697760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 697760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 697760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 56458
 
8.1%
51458
 
7.4%
i 41434
 
5.9%
r 35230
 
5.0%
S 31518
 
4.5%
a 30818
 
4.4%
L 27778
 
4.0%
T 26700
 
3.8%
t 21824
 
3.1%
o 21029
 
3.0%
Other values (69) 353513
50.7%

drivetrain
Categorical

Distinct14
Distinct (%)< 0.1%
Missing493
Missing (%)0.5%
Memory size716.1 KiB
All-wheel Drive
43862 
Front-wheel Drive
18652 
Four-wheel Drive
14890 
Rear-wheel Drive
10511 
AWD
 
1882
Other values (9)
 
1356

Length

Max length58
Median length17
Mean length15.272684
Min length3

Characters and Unicode

Total characters1392151
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAll-wheel Drive
2nd rowFront-wheel Drive
3rd rowRear-wheel Drive
4th rowFront-wheel Drive
5th rowFront-wheel Drive

Common Values

ValueCountFrequency (%)
All-wheel Drive 43862
47.9%
Front-wheel Drive 18652
20.4%
Four-wheel Drive 14890
 
16.2%
Rear-wheel Drive 10511
 
11.5%
AWD 1882
 
2.1%
FWD 614
 
0.7%
4WD 341
 
0.4%
Unknown 225
 
0.2%
RWD 169
 
0.2%
4x2 2
 
< 0.1%
Other values (4) 5
 
< 0.1%
(Missing) 493
 
0.5%

Length

2024-05-19T23:58:54.437124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drive 87920
49.1%
all-wheel 43864
24.5%
front-wheel 18652
 
10.4%
four-wheel 14890
 
8.3%
rear-wheel 10511
 
5.9%
awd 1882
 
1.1%
fwd 614
 
0.3%
4wd 341
 
0.2%
unknown 225
 
0.1%
rwd 169
 
0.1%
Other values (9) 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1392151
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1392151
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1392151
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 274274
19.7%
l 175650
12.6%
r 131977
9.5%
D 90927
 
6.5%
w 88141
 
6.3%
87928
 
6.3%
i 87927
 
6.3%
h 87921
 
6.3%
v 87920
 
6.3%
- 87918
 
6.3%
Other values (22) 191568
13.8%

dealer_name
Text

MISSING 

Distinct597
Distinct (%)0.7%
Missing941
Missing (%)1.0%
Memory size716.1 KiB
2024-05-19T23:58:54.857923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length86
Median length54
Mean length24.058817
Min length6

Characters and Unicode

Total characters2182255
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)0.1%

Sample

1st rowCastle Rock Chevrolet GMC
2nd rowNew Smyrna Chrysler Jeep Dodge RAM
3rd rowMercedes-Benz of Farmington
4th rowKingman Honda
5th rowMcSweeney Chevrolet GMC Clanton
ValueCountFrequency (%)
of 28047
 
8.4%
chicago 8545
 
2.6%
auto 8253
 
2.5%
dodge 7557
 
2.3%
chrysler 7393
 
2.2%
jeep 7391
 
2.2%
ram 7389
 
2.2%
ford 6413
 
1.9%
chevrolet 6157
 
1.8%
motors 5387
 
1.6%
Other values (625) 241047
72.3%
2024-05-19T23:58:55.458146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2182255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2182255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2182255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
242874
 
11.1%
e 173898
 
8.0%
o 171979
 
7.9%
a 147574
 
6.8%
r 129685
 
5.9%
n 98845
 
4.5%
l 97037
 
4.4%
t 92316
 
4.2%
i 91764
 
4.2%
s 85745
 
3.9%
Other values (57) 850538
39.0%

dealer_zip
Real number (ℝ)

MISSING 

Distinct285
Distinct (%)0.3%
Missing941
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean59746.335
Minimum1060
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:55.744261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1060
5-th percentile60004
Q160126
median60445
Q360532
95-th percentile60659
Maximum99301
Range98241
Interquartile range (IQR)406

Descriptive statistics

Standard deviation3239.2381
Coefficient of variation (CV)0.054216516
Kurtosis48.749856
Mean59746.335
Median Absolute Deviation (MAD)197
Skewness-4.7607426
Sum5.4192913 × 109
Variance10492664
MonotonicityNot monotonic
2024-05-19T23:58:55.909745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60540 5649
 
6.2%
60515 4478
 
4.9%
60525 3182
 
3.5%
60126 3180
 
3.5%
46322 2660
 
2.9%
60453 2643
 
2.9%
60477 2600
 
2.8%
60559 2436
 
2.7%
60035 2210
 
2.4%
60074 2115
 
2.3%
Other values (275) 59552
65.0%
ValueCountFrequency (%)
1060 2
< 0.1%
1201 1
< 0.1%
2601 2
< 0.1%
2886 1
< 0.1%
3878 1
< 0.1%
4072 1
< 0.1%
4074 2
< 0.1%
5158 2
< 0.1%
6066 1
< 0.1%
6405 1
< 0.1%
ValueCountFrequency (%)
99301 1
 
< 0.1%
99201 2
< 0.1%
99019 1
 
< 0.1%
98037 1
 
< 0.1%
98036 1
 
< 0.1%
97005 2
< 0.1%
95757 1
 
< 0.1%
95661 1
 
< 0.1%
95407 3
< 0.1%
95129 1
 
< 0.1%

mileage
Real number (ℝ)

MISSING  ZEROS 

Distinct17169
Distinct (%)19.2%
Missing2366
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean30085.104
Minimum0
Maximum962839
Zeros3630
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:56.079457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median3146.5
Q351176
95-th percentile119510
Maximum962839
Range962839
Interquartile range (IQR)51169

Descriptive statistics

Standard deviation43318.389
Coefficient of variation (CV)1.4398617
Kurtosis5.6666895
Mean30085.104
Median Absolute Deviation (MAD)3146.5
Skewness1.8043339
Sum2.685998 × 109
Variance1.8764829 × 109
MonotonicityNot monotonic
2024-05-19T23:58:56.252247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6126
 
6.7%
10 5627
 
6.1%
0 3630
 
4.0%
3 3280
 
3.6%
6 2984
 
3.3%
2 2263
 
2.5%
1 2141
 
2.3%
7 2093
 
2.3%
11 1908
 
2.1%
4 1745
 
1.9%
Other values (17159) 57483
62.7%
(Missing) 2366
 
2.6%
ValueCountFrequency (%)
0 3630
4.0%
1 2141
 
2.3%
2 2263
 
2.5%
3 3280
3.6%
4 1745
 
1.9%
5 6126
6.7%
6 2984
3.3%
7 2093
 
2.3%
8 1376
 
1.5%
9 1329
 
1.5%
ValueCountFrequency (%)
962839 1
 
< 0.1%
440911 2
 
< 0.1%
426586 2
 
< 0.1%
398677 2
 
< 0.1%
385223 2
 
< 0.1%
350017 5
< 0.1%
324349 3
< 0.1%
318260 3
< 0.1%
317568 2
 
< 0.1%
317508 7
< 0.1%

make
Text

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:56.514297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.3136089
Min length3

Characters and Unicode

Total characters578617
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChevrolet
2nd rowRAM
3rd rowMercedes-Benz
4th rowHonda
5th rowChevrolet
ValueCountFrequency (%)
ford 7775
 
8.4%
chevrolet 7763
 
8.3%
mercedes-benz 6184
 
6.6%
bmw 5661
 
6.1%
nissan 5527
 
5.9%
hyundai 5053
 
5.4%
jeep 4987
 
5.4%
volkswagen 4588
 
4.9%
subaru 3535
 
3.8%
audi 3275
 
3.5%
Other values (56) 38718
41.6%
2024-05-19T23:58:57.000149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 578617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 578617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 578617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 65392
 
11.3%
a 45460
 
7.9%
o 38942
 
6.7%
d 34109
 
5.9%
r 32533
 
5.6%
n 29429
 
5.1%
s 28416
 
4.9%
i 24881
 
4.3%
l 22962
 
4.0%
u 21932
 
3.8%
Other values (36) 234561
40.5%

bodystyle
Categorical

Distinct10
Distinct (%)< 0.1%
Missing517
Missing (%)0.6%
Memory size716.1 KiB
SUV
50928 
Sedan
16807 
Pickup Truck
6833 
Coupe
 
4651
Cargo Van
 
3342
Other values (5)
8568 

Length

Max length13
Median length3
Mean length4.9849993
Min length3

Characters and Unicode

Total characters454278
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUV
2nd rowCargo Van
3rd rowCargo Van
4th rowSUV
5th rowSUV

Common Values

ValueCountFrequency (%)
SUV 50928
55.6%
Sedan 16807
 
18.3%
Pickup Truck 6833
 
7.5%
Coupe 4651
 
5.1%
Cargo Van 3342
 
3.6%
Hatchback 3305
 
3.6%
Convertible 3242
 
3.5%
Wagon 1049
 
1.1%
Passenger Van 779
 
0.9%
Minivan 193
 
0.2%
(Missing) 517
 
0.6%

Length

2024-05-19T23:58:57.154663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:58:57.290220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
suv 50928
49.9%
sedan 16807
 
16.5%
pickup 6833
 
6.7%
truck 6833
 
6.7%
coupe 4651
 
4.6%
van 4121
 
4.0%
cargo 3342
 
3.3%
hatchback 3305
 
3.2%
convertible 3242
 
3.2%
wagon 1049
 
1.0%
Other values (2) 972
 
1.0%

Most occurring characters

ValueCountFrequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 454278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 454278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 454278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 67735
14.9%
V 55049
12.1%
U 50928
11.2%
a 32901
 
7.2%
e 29500
 
6.5%
n 26384
 
5.8%
c 20276
 
4.5%
u 18317
 
4.0%
k 16971
 
3.7%
d 16807
 
3.7%
Other values (18) 119410
26.3%

cat
Categorical

MISSING 

Distinct39
Distinct (%)< 0.1%
Missing1301
Missing (%)1.4%
Memory size716.1 KiB
crossover_compact
16603 
luxurysuv_crossover
10984 
crossover_midsize
7416 
suv_midsize
5586 
luxurypassenger_standard
5272 
Other values (34)
44484 

Length

Max length28
Median length24
Mean length16.751353
Min length8

Characters and Unicode

Total characters1513401
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowev_crossover_midsize
2nd rowvan_fullsize
3rd rowvan_fullsize
4th rowcrossover_compact
5th rowcrossover_midsize

Common Values

ValueCountFrequency (%)
crossover_compact 16603
18.1%
luxurysuv_crossover 10984
12.0%
crossover_midsize 7416
 
8.1%
suv_midsize 5586
 
6.1%
luxurypassenger_standard 5272
 
5.8%
truck_fullsize 4542
 
5.0%
sedan_compact 4072
 
4.4%
van_fullsize 3918
 
4.3%
luxurypassenger_plus 3820
 
4.2%
sedan_midsize 3546
 
3.9%
Other values (29) 24586
26.8%

Length

2024-05-19T23:58:57.476038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crossover_compact 16603
18.4%
luxurysuv_crossover 10984
12.2%
crossover_midsize 7416
 
8.2%
suv_midsize 5586
 
6.2%
luxurypassenger_standard 5272
 
5.8%
truck_fullsize 4542
 
5.0%
sedan_compact 4072
 
4.5%
van_fullsize 3918
 
4.3%
luxurypassenger_plus 3820
 
4.2%
sedan_midsize 3546
 
3.9%
Other values (29) 24586
27.2%

Most occurring characters

ValueCountFrequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1513401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1513401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1513401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 176397
11.7%
r 135879
 
9.0%
e 122757
 
8.1%
o 114537
 
7.6%
c 111454
 
7.4%
u 109141
 
7.2%
_ 95105
 
6.3%
v 80896
 
5.3%
a 73409
 
4.9%
i 59978
 
4.0%
Other values (15) 433848
28.7%

vin
Text

Distinct40001
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:57.733166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.993704
Min length7

Characters and Unicode

Total characters1557405
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14282 ?
Unique (%)15.6%

Sample

1st row3GNKDCRJ6RS227894
2nd row3C6LRVDG0RE118763
3rd rowW1Y4KCHY8RT178723
4th row5J6RS3H44RL004214
5th row3GNAXHEG1RL299011
ValueCountFrequency (%)
sajwa4ec0emb52401 14
 
< 0.1%
km8nu73c98u061498 14
 
< 0.1%
wdbwk56f46f111412 14
 
< 0.1%
1c3adebz8dv400466 14
 
< 0.1%
yv4952bl7b1101669 14
 
< 0.1%
wbsbf932xseh07679 14
 
< 0.1%
sbm16aea7pw001459 14
 
< 0.1%
yv1672mc5cj127325 14
 
< 0.1%
wvwhv71k37w053151 14
 
< 0.1%
wp1ae2a29dla14757 14
 
< 0.1%
Other values (39991) 91506
99.8%
2024-05-19T23:58:58.139863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1557405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1557405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1557405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 127758
 
8.2%
3 94251
 
6.1%
0 91937
 
5.9%
2 88928
 
5.7%
4 86504
 
5.6%
5 86304
 
5.5%
7 72060
 
4.6%
6 70286
 
4.5%
8 67567
 
4.3%
R 64838
 
4.2%
Other values (26) 706972
45.4%
Distinct4736
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
2024-05-19T23:58:58.562391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length97
Median length86
Mean length23.129891
Min length7

Characters and Unicode

Total characters2119762
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique464 ?
Unique (%)0.5%

Sample

1st rowChevrolet:Blazer EV:RS
2nd rowRAM:ProMaster 2500:High Roof
3rd rowMercedes-Benz:Sprinter 2500:High Roof
4th rowHonda:CR-V:EX
5th rowChevrolet:Equinox:LS
ValueCountFrequency (%)
4matic 2964
 
1.7%
premium 2711
 
1.5%
s 2686
 
1.5%
se 2582
 
1.5%
plus 2246
 
1.3%
jeep:grand 2198
 
1.2%
roof 1717
 
1.0%
package 1535
 
0.9%
xdrive 1462
 
0.8%
mercedes-benz:amg 1320
 
0.7%
Other values (4046) 155640
87.9%
2024-05-19T23:58:59.212619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2119762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2119762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2119762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
: 183292
 
8.6%
e 166055
 
7.8%
a 126354
 
6.0%
r 114485
 
5.4%
o 93474
 
4.4%
i 90624
 
4.3%
85271
 
4.0%
n 75657
 
3.6%
s 68238
 
3.2%
d 59366
 
2.8%
Other values (70) 1056946
49.9%

fuel_type
Categorical

IMBALANCE 

Distinct18
Distinct (%)< 0.1%
Missing656
Missing (%)0.7%
Memory size716.1 KiB
Gasoline
78530 
Electric
 
4864
Hybrid
 
4188
Diesel
 
1930
E85 Flex Fuel
 
1347
Other values (13)
 
131

Length

Max length29
Median length8
Mean length7.9477525
Min length6

Characters and Unicode

Total characters723166
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowElectric
2nd rowGasoline
3rd rowDiesel
4th rowGasoline
5th rowGasoline

Common Values

ValueCountFrequency (%)
Gasoline 78530
85.7%
Electric 4864
 
5.3%
Hybrid 4188
 
4.6%
Diesel 1930
 
2.1%
E85 Flex Fuel 1347
 
1.5%
Plug-In Hybrid 33
 
< 0.1%
Flexible Fuel 21
 
< 0.1%
Bio Diesel 17
 
< 0.1%
Gasoline Fuel 16
 
< 0.1%
Electric with Ga 13
 
< 0.1%
Other values (8) 31
 
< 0.1%
(Missing) 656
 
0.7%

Length

2024-05-19T23:58:59.426932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gasoline 78546
83.7%
electric 4878
 
5.2%
hybrid 4224
 
4.5%
diesel 1947
 
2.1%
fuel 1386
 
1.5%
e85 1347
 
1.4%
flex 1347
 
1.4%
plug-in 38
 
< 0.1%
flexible 21
 
< 0.1%
bio 17
 
< 0.1%
Other values (12) 77
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 723166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 723166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 723166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 90139
12.5%
i 89668
12.4%
l 88218
12.2%
s 80512
11.1%
a 78600
10.9%
n 78596
10.9%
G 78577
10.9%
o 78564
10.9%
c 9778
 
1.4%
r 9124
 
1.3%
Other values (28) 41390
5.7%

stock_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
Used
46230 
New
45416 

Length

Max length4
Median length4
Mean length3.504441
Min length3

Characters and Unicode

Total characters321168
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew
2nd rowNew
3rd rowNew
4th rowNew
5th rowNew

Common Values

ValueCountFrequency (%)
Used 46230
50.4%
New 45416
49.6%

Length

2024-05-19T23:58:59.579728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:58:59.682896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
used 46230
50.4%
new 45416
49.6%

Most occurring characters

ValueCountFrequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 321168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 321168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 321168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 91646
28.5%
U 46230
14.4%
s 46230
14.4%
d 46230
14.4%
N 45416
14.1%
w 45416
14.1%

exterior_color
Text

MISSING 

Distinct2511
Distinct (%)2.8%
Missing1042
Missing (%)1.1%
Memory size716.1 KiB
2024-05-19T23:58:59.940886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length48
Median length41
Mean length14.797581
Min length1

Characters and Unicode

Total characters1340720
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)0.3%

Sample

1st rowsterling_gray_metallic
2nd rowbright_white_clearcoat
3rd rowblue_grey
4th rowradiant_red_metallic
5th rowsummit_white
ValueCountFrequency (%)
black 6103
 
6.7%
white 3580
 
4.0%
gray 2595
 
2.9%
summit_white 2372
 
2.6%
bright_white_clearcoat 2223
 
2.5%
silver 1591
 
1.8%
blue 1581
 
1.7%
red 1375
 
1.5%
oxford_white 1140
 
1.3%
black_sapphire_metallic 930
 
1.0%
Other values (2500) 67114
74.1%
2024-05-19T23:59:00.611536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1340720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1340720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1340720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 137200
 
10.2%
a 134181
 
10.0%
e 133960
 
10.0%
_ 116788
 
8.7%
i 108200
 
8.1%
t 101918
 
7.6%
c 86900
 
6.5%
r 84084
 
6.3%
m 51433
 
3.8%
b 40067
 
3.0%
Other values (37) 345989
25.8%

page_channel
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size716.1 KiB
shopping
91646 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters733168
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshopping
2nd rowshopping
3rd rowshopping
4th rowshopping
5th rowshopping

Common Values

ValueCountFrequency (%)
shopping 91646
100.0%

Length

2024-05-19T23:59:00.771770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-19T23:59:00.868864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
shopping 91646
100.0%

Most occurring characters

ValueCountFrequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 733168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 733168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 733168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 183292
25.0%
s 91646
12.5%
h 91646
12.5%
o 91646
12.5%
i 91646
12.5%
n 91646
12.5%
g 91646
12.5%

Interactions

2024-05-19T23:58:45.818867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:41.778235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.520632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.340059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.132985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.028339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.048235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:41.909349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.629390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.474485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.249823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.131005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.248676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.019386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.746016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.586671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.388545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.290617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.539829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.130582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.863708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.696281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.568187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.402825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.777033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.265406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.000341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.902309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.694231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.562667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:46.914548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:42.406604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:43.182208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.020476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:44.815932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-19T23:58:45.697911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-19T23:58:47.138203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-19T23:58:47.658785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0msrpyearcanonical_mmtymodellocal_zoneinterior_coloraff_codepriceprice_badgetrimdrivetraindealer_namedealer_zipmileagemakebodystylecatvincanonical_mmtfuel_typestock_typeexterior_colorpage_channel
0057215.02024Chevrolet:Blazer EV:RS:2024Blazer EVNaNblacknational54595.0NaNRSAll-wheel DriveCastle Rock Chevrolet GMC80104.00.0ChevroletSUVev_crossover_midsize3GNKDCRJ6RS227894Chevrolet:Blazer EV:RSElectricNewsterling_gray_metallicshopping
1158845.02024RAM:ProMaster 2500:High Roof:2024ProMaster 2500NaNblacknational52446.0NaNHigh RoofFront-wheel DriveNew Smyrna Chrysler Jeep Dodge RAM32168.00.0RAMCargo Vanvan_fullsize3C6LRVDG0RE118763RAM:ProMaster 2500:High RoofGasolineNewbright_white_clearcoatshopping
2258795.02024Mercedes-Benz:Sprinter 2500:High Roof:2024Sprinter 2500NaNNaNnational54295.0NaNHigh RoofRear-wheel DriveMercedes-Benz of Farmington84025.08.0Mercedes-BenzCargo Vanvan_fullsizeW1Y4KCHY8RT178723Mercedes-Benz:Sprinter 2500:High RoofDieselNewblue_greyshopping
3333815.02024Honda:CR-V:EX:2024CR-VNaNgraynationalNaNNaNEXFront-wheel DriveKingman Honda86409.07.0HondaSUVcrossover_compact5J6RS3H44RL004214Honda:CR-V:EXGasolineNewradiant_red_metallicshopping
4427995.02024Chevrolet:Equinox:LS:2024EquinoxNaNmedium_ash_graynational24803.0NaNLSFront-wheel DriveMcSweeney Chevrolet GMC Clanton35045.00.0ChevroletSUVcrossover_midsize3GNAXHEG1RL299011Chevrolet:Equinox:LSGasolineNewsummit_whiteshopping
5583630.02024Audi:Q8 e-tron:Premium:2024Q8 e-tronNaNpearl_beigenational83630.0NaNPremiumAll-wheel DriveAudi Stuart34997.020.0AudiSUVev_crossover_midsizeWA15AAGE4RB021424Audi:Q8 e-tron:PremiumElectricNewglacier_white_metallicshopping
6633610.02024Mitsubishi:Eclipse Cross:SEL:2024Eclipse CrossNaNgraynational33610.0NaNSELFour-wheel DriveMcClinton Auto Group26101.05.0MitsubishiSUVcrossover_compactJA4ATWAA2RZ046423Mitsubishi:Eclipse Cross:SELGasolineNewmercury_gray_metallicshopping
7750185.02024Dodge:Hornet:R/T Plus:2024HornetNaNblacknational40185.0NaNR/T PlusAll-wheel DriveDon Jackson CDJR North30028.016.0DodgeSUVhybrid_suvZACPDFDW9R3A24025Dodge:Hornet:R/T PlusHybridNewblue_steelshopping
8827825.02024Nissan:Kicks:SR:2024KicksNaNcharcoalnational27825.0NaNSRFront-wheel DriveHalladay Nissan82001.06.0NissanSUVcrossover_compact3N1CP5DV6RL526633Nissan:Kicks:SRGasolineNewscarlet_ember_tintcoatshopping
9953727.02024Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-Line:2024Atlas Cross SportNaNblack_w/_blue_crustnational50727.0NaN2.0T SEL Premium R-LineAll-wheel DriveAutoNation Volkswagen Las Vegas89146.010.0VolkswagenSUVsuv_midsize1V2FE2CA0RC238064Volkswagen:Atlas Cross Sport:2.0T SEL Premium R-LineGasolineNewplatinum_gray_metallicshopping
Unnamed: 0msrpyearcanonical_mmtymodellocal_zoneinterior_coloraff_codepriceprice_badgetrimdrivetraindealer_namedealer_zipmileagemakebodystylecatvincanonical_mmtfuel_typestock_typeexterior_colorpage_channel
916369163636669.02024Subaru:Crosstrek:Wilderness:2024CrosstrekNaNblacknational36669.0NaNWildernessAll-wheel DriveGerald Subaru of Naperville60540.06.0SubaruSUVcrossover_compact4S4GUHU63R3780034Subaru:Crosstrek:WildernessGasolineNewsun_blaze_pearlshopping
916379163751443.02024Volkswagen:Atlas Cross Sport:2.0T SEL R-Line:2024Atlas Cross SportNaNmauro_brownnational47013.0NaN2.0T SEL R-LineAll-wheel DriveVolkswagen of Downtown Chicago60610.07.0VolkswagenSUVsuv_midsize1V2AE2CA4RC213467Volkswagen:Atlas Cross Sport:2.0T SEL R-LineGasolineNewplatinum_gray_metallicshopping
9163891638NaN2022Jeep:Wagoneer:Series I:2022WagoneerNaNglobal_blacknational49991.0NaNSeries IFour-wheel DriveLaurel BMW of Westmont60559.017815.0JeepSUVsuv_midsize1C4SJVAT7NS227323Jeep:Wagoneer:Series IGasolineUsedriverrock_greenshopping
9163991639106555.02024Lincoln:Navigator L:Reserve:2024Navigator LNaNblack_onyxnational104555.0NaNReserveFour-wheel DriveFox Lincoln60647.04.0LincolnSUVluxurysuv_suv5LMJJ3LG7REL01031Lincoln:Navigator L:ReserveGasolineNewsilver_radiance_metallicshopping
916409164034104.02024Volkswagen:Taos:1.5T SE:2024TaosNaNblacknational31234.0NaN1.5T SEAll-wheel DriveThe Autobarn Volkswagen of Countryside60525.06.0VolkswagenSUVcrossover_compact3VVVX7B2XRM053891Volkswagen:Taos:1.5T SEGasolineNewpure_white_/_black_roofshopping
916419164157135.02024RAM:1500:Tradesman:20241500NaNblacknational48135.0NaNTradesmanFour-wheel DriveZeigler Chrysler Dodge Jeep Ram of Schaumburg60195.014.0RAMPickup Trucktruck_fullsize1C6SRFGTXRN165708RAM:1500:TradesmanGasolineNewbillet_silver_metallic_clearcoatshopping
91642916420.02010Volkswagen:Eos:Komfort:2010EosNaNtitan_blacknational11950.0NaNKomfortFront-wheel DriveNet Motorcars60101.086868.0VolkswagenConvertiblecoupeconvertible_convertibleWVWBA7AH7AV014923Volkswagen:Eos:KomfortGasolineUsedreflex_silver_metallicshopping
916439164383695.02024RAM:1500:Longhorn:20241500NaNmountain_brownnational73195.0NaNLonghornFour-wheel DriveZeigler Chrysler Dodge Jeep Ram of Schaumburg60195.015.0RAMPickup Trucktruck_fullsize1C6SRFKT9RN212671RAM:1500:LonghornGasolineNewdiamond_black_crystal_pearlcoatshopping
916449164475280.02024Chevrolet:Suburban:LT:2024SuburbanNaNblacknational71122.0NaNLTFour-wheel DrivePhillips Chevrolet60423.03.0ChevroletSUVsuv_fullsize1GNSKCKD4RR151802Chevrolet:Suburban:LTGasolineNewblackshopping
916459164533010.02024Ford:Escape:Active:2024EscapeNaNebony_blacknational31000.0NaNActiveAll-wheel DriveTasca Ford Midlothian60445.012.0FordSUVcrossover_compact1FMCU9GN0RUA09502Ford:Escape:ActiveGasolineNewblack_metallicshopping